心力衰竭中的机器学习：准备好迎接黄金时代。
Machine learning in heart failure: ready for prime time.
作者信息
Awan Saqib Ejaz, Sohel Ferdous, Sanfilippo Frank Mario, Bennamoun Mohammed, Dwivedi Girish
机构信息
School of Computer Science and Software Engineering, The University of Western Australia.
School of Engineering and Information technology, Murdoch University.
出版信息
Curr Opin Cardiol. 2018 Mar;33(2):190-195. doi: 10.1097/HCO.0000000000000491.
PURPOSE OF REVIEW
The aim of this review is to present an up-to-date overview of the application of machine learning methods in heart failure including diagnosis, classification, readmissions and medication adherence.
RECENT FINDINGS
Recent studies have shown that the application of machine learning techniques may have the potential to improve heart failure outcomes and management, including cost savings by improving existing diagnostic and treatment support systems. Recently developed deep learning methods are expected to yield even better performance than traditional machine learning techniques in performing complex tasks by learning the intricate patterns hidden in big medical data.
SUMMARY
The review summarizes the recent developments in the application of machine and deep learning methods in heart failure management.
综述目的
本综述旨在对机器学习方法在心力衰竭中的应用进行最新概述，包括诊断、分类、再入院和药物依从性。
最新发现
近期研究表明，机器学习技术的应用可能有潜力改善心力衰竭的治疗结果和管理，包括通过改进现有的诊断和治疗支持系统来节省成本。预计最近开发的深度学习方法在执行复杂任务时，通过学习隐藏在大量医学数据中的复杂模式，将比传统机器学习技术产生更好的性能。
总结
本综述总结了机器学习和深度学习方法在心力衰竭管理应用中的最新进展。
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